The regulatory risks of AI in medical translation: Precision vs efficiency in the EU MDR era

Artificial intelligence is rapidly reshaping how organisations approach multilingual documentation. In Life Sciences, where clinical timelines are compressed, and regulatory expectations continue to expand, AI in medical translation can appear to offer a compelling solution: faster turnaround times, lower costs, and scalable output.

However, in regulated industries governed by the EU Medical Device Regulation (MDR) and stringent quality standards such as ISO 17100, speed alone is not a competitive advantage. Precision, traceability, and accountability remain non-negotiable. When patient safety and regulatory approval are at stake, the risks associated with uncontrolled AI use must be carefully evaluated. This article explores where AI can support medical translation workflows—and where it introduces severe regulatory exposure.

The allure of speed: Why AI in medical translation appeals to CROs

The pharmaceutical and biotech sectors are currently navigating a "perfect storm" of increased data volume and compressed timelines. Generative AI and Large Language Models (LLMs) offer unprecedented scalability, tempting Contract Research Organisations (CROs) to automate the translation of vast technical dossiers.

While the promise of raw throughput is undeniable, the medical field operates on a zero-error threshold. Efficiency must never be confused with effectiveness. In practice, many AI models are probabilistic—they guess the next word based on patterns. In a regulatory dossier, guessing is not an option.

Why AI in medical translation fails ISO 17100 and MDR compliance requirements

The core challenge with raw AI output is the lack of accountability. Under a professional ISO 17100-certified translation process, human revision is not a "quality check"; it is a mandatory legal safeguard to ensure technical accuracy.

AI models lack the cognitive ability to perform linguistic validation—a critical step in ensuring clinical outcomes are culturally appropriate. A simple example of what this might lead to: a manufacturer using unedited AI for an IFU might find that "lead" (the metal) was translated as "lead" (to guide), potentially causing a device failure. The European Medicines Agency (EMA) explicitly warns that AI requires "close human supervision" to mitigate risks.

Terminology hazards: AI hallucinations in medical device translation

AI hallucinations pose the most significant barrier to safe AI adoption in medical terminology translations. Because Large Language Models do not understand clinical science, they can introduce several high-risk errors:

1. Terminology drift: Subtle shifts in meaning that can change the interpretation of dosages, procedures or device functions.
2. Context collapse: Failure to distinguish between similar terms used differently across medical specialities.
3. False cognates: Misinterpretation of terms that appear linguistically similar but carry different regulatory or clinical implications.

In a regulatory environment where precision is mandatory, even a single hallucinated term in a clinical protocol can lead to the immediate suspension of a clinical trial by national authorities.

EU MDR language requirements: Precision as a legal obligation

Compliance with MDR language requirements is a legal prerequisite for market access in Europe. According to EU MDR 2017/745, all IFUs and labelling must be provided in the official language of the Member State where the device is marketed.

In the Netherlands, the IGJ (Health and Youth Care Inspectorate) strictly enforces that safety information is accessible. If an AI-driven translation fails to convey a safety warning with absolute precision, the manufacturer faces product recalls across the European Union.

MTPE vs 100% human translation: A risk‑based framework

Machine Translation Post-Editing (MTPE) is effective for low-risk internal documents. However, high-risk regulatory translation services for SmPCs or surgical manuals demand 100% human expertise.